Last week, a different young person in Egypt captured my attention when, during an interview, he rattled off a more sophisticated analysis of politics in Egypt than CNN by several times over.  He made an impression, and changed my mind about Egyptian politics.

It seems like the pace of single individuals  impacting a much larger group, whether a company, country, or the world, is increasing. Some portrayed as villains… Ed Snowden sitting in a Moscow neutral zone. Or an office of the IRS with a bad idea for fighting not-for-profit fraud. Some heros… Like Talia or Elon Musk, who,  is executing on revolutionary ideas about travel every month. 

Many of the tools we use in talent analytics focus on central tendency and deviation from that tendency. I use means testing about as well as the next guy, but it tells me less than needed about exceptional individuals (in either positive or negative directions).  And those are the stories that we need to tell more in analytics. These are questions like… What is the best possible outcome for (X)?  What is the worst?

An example… Engagement surveys tell us about employee attitudes expressed in average % favorable / unfavorable scores. The instinct of many managers is that they should all read 100% favorable. (I can be guilty of that too. It’s hard not to take results personally.) Given a sufficiently large population, however, it’s fair to ask what the best possible outcome for a question could be. 200 people will almost never score an amazing leader perfectly. People are just not that uniform. Statistically, what’s the best number you could expect? As analysts, we need to know what exceptional performance really  looks like. 

I’m a fan of Nassim Taleb’s work The Black Swan, and drawn to his conclusion that the extremes matter more than we assume in everyday work. I’m wondering today if we should be looking for black mice, not black swans. 

Why mice? We all know black mice exist. There are probably  lots of them, but we don’t see them that frequently. And they make smaller, but tangible impacts on an organization. Someone  said to me once, “You can measure the average with employees all you want, but the exceptions will make or break your business.”  Seems like this is becoming more true today vs. yesterday. 

Talia and Ed and the little Egyptian boy will be out of the news cycle shortly, replaced by others. Some will propel organizations to greatness. Some will hurt them. As analysts, what’s our role in understanding the extremes? That’s a post for another day.

Heard on the radio this morning a great quote from the owner of Russ & Daughters, a 4th generation seller of lox, whitefish, and other spreads: 

“We’re dealing with a finicky product,” Federman says. “We’re talking about fish, most of it wild. Every fish is different. Every customer is different. Every employee is different. And the idea is to be able to line up fish and customer and counterman perfectly hundreds of times a day.”

Yup. Talent Management in a nutshell (or net)

http://www.npr.org/blogs/thesalt/2013/03/03/173264635/family-keeps-jewish-soulfood-alive-at-new-york-appetizing-store 

While this is not a talent management book, the work has profound implications for how we think about the work done in HR circles. For example, Nass has proven that the most common method of doing performance reviews by managers (he calls it the “compliment sandwich” – compliment, criticism, compliment) actually leaves the reviewed employee with the wrong message. They remember the last compliment, not the thing to change. 

There’s a nice piece on Nass’ work (and others) from the radio today here. 

Happy Holidays everyone.

And may both you and your data both grow and prosper exponentially in 2013.

Compliments of xkcd.com

I don’t know what the plan is, but I would love to get my hands on the big data that results!

Happy Thanksgiving!

Cooking can be surprisingly forgiving | Science & Society | Science News.

1. The team that developed it was 160 people strong, with economists, financial folks, hr analytics folks, generalists, and risk managers.

2. We know that it’s impossible to factor every critical HC metric for every type of business into a standard. The ANSI method discourages this. It looks for a minimally effective standard. If we knew more today about a company than yesterday, would these be the 6 items we would choose?

3. In a world far removed from most of our everyday lives, this type of disclosure has already been written, but without HRs perspective. The intangibles and sustainability community are already releasing data like this. But we can help make it better with more expertise in human capital.

And finally, the most critical.. ANSI standards are voluntary. No one is forcing us to disclose data we wish not to.

Please do shoot me questions or post them below!

I didn’t engage him heavily, it was after all, a stranger on a train… but, what I was thinking was, ” Wow, that would be a terrible doctor that only relied on the evidence provided from a specific set of tests he/she ordered.” Would you keep going to a doctor that looked past your feedback and just used the results of a test?

This, to me, is one of the fundamental misunderstandings of any evidence-based decision.  I don’t know if it’s more a fear by people who don’t use the data more than a reality of inexperienced practitioners, but proper consumption of evidence certainly merits discussion.

Good consumers of data are interested in both the digital and the analog – they know that you can’t see the whole picture through either one. Digital results can skew your opinion just as easily as analog ones.

When dealing with decisions regarding humans (medicine, marketing, talent management) – we ignore anecdotes at our peril.  A friend of mine just told me an (unvalidated by me) story on a marketing campaign for an air freshener. They had loads of data about customer perceptions of the product, but it wasn’t until they followed around the product’s #1 fan for a day that they realized what the marketing hook was. They couldn’t see her son’s stinky socks in the data, and apparently, for this decision, that’s what they needed.

Any time we’re dealing with data about human behavior, I always find it helpful to stop for a moment and recognize that behind the numbers are people with feelings, with kids ( their socks), and certainly with their own free will.

That’s how you see the forest through the trees and make the right decision.

Haas School of Business Researcher Henry Chesbrough talking about Open Innovation.